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The extant <i>Chaoborus</i> assemblage can be assessed using subfossil mandibles

2010· article· en· W1515037643 on OpenAlexaffabout
Roberto Quinlan, John P. Smol

Bibliographic record

VenueFreshwater Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsQueen's UniversityYork University
FundersMinistry of Environment
KeywordsSubfossilEcologyHypolimnionSpecies richnessBiologyPaleontologyEutrophication

Abstract

fetched live from OpenAlex

Summary 1. Assemblages of Chaoborus were examined in 80 thermally stratified southern central Canadian Shield lakes to explore whether subfossil mandibles could be useful in assemblage‐level studies of Chaoborus . 2. Chaoborus ( Sayomyia ) (probably Chaoborus punctipennis in this study region) and Chaoborus flavicans were the most common taxa recorded, while Chaoborus trivittatus was rarer. Chaoborus americanus was not recorded in subfossil assemblages, because no fishless lakes were included in this study. Chaoborus flavicans had higher relative abundances (%) in lakes with higher dissolved organic carbon (DOC), probably because of reduced fish predation in less transparent water. 3. Results from logistic regression indicate that patterns of presence/absence for Chaoborus mandibles in the study lakes were influenced primarily by hypolimnetic oxygen concentration, probably because of the presence or extent of a hypolimnetic refugium from fish predation. 4. Chaoborus species richness in lakes, derived from subfossil assemblages, did not differ significantly from species richness estimates derived from plankton sampling with a net. Patterns of dominance and coexistence [e.g. the widespread co‐occurrence of C. flavicans and C. ( Sayomyia )] determined from subfossil assemblages agreed with previous studies of the contemporary living assemblage. 5. These results suggest that subfossil assemblages may be used as an alternative to nocturnal plankton sampling to carry out research on the community ecology of Chaoborus . 6. We propose a hierarchical conceptual model of assemblage‐level patterns of Chaoborus in temperate lakes. Chaoborus americanus dominates in fishless lakes, whereas in lakes with fish Chaoborus is typically absent where there is no anoxic hyplimnion. In lakes with anoxic strata, C. trivittatus tends to dominate in lakes with few fish; in the remaining lakes, C. flavicans and C. ( Sayomyia ) dominate, although C. flavicans is more relatively abundant in lakes with lower water clarity (higher DOC).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.265
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2010
Admission routes2
Has abstractyes

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